AnatoMix: Anatomy-aware Data Augmentation for Multi-organ Segmentation

arXiv:2403.03326v1 Announce Type: cross
Abstract: Multi-organ segmentation in medical images is a widely researched task and can save much manual efforts of clinicians in daily routines. Automating the organ segmentation process using deep learning (DL) is a promising solution and state-of-the-art segmentation models are achieving promising accuracy. In this work, We proposed a novel data augmentation strategy for increasing the generalizibility of multi-organ segmentation datasets, namely AnatoMix. By object-level matching and manipulation, our method is able to generate new images …

abstract accuracy art arxiv augmentation clinicians cs.cv daily data deep learning eess.iv images medical process save segmentation solution state type work